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AI Realty Chatbot

Client

Leading luxury property developer in the United States. Their portfolio comprises residential, commercial, and leisure properties across North America.

Industry

Realty

Offering

Our solution is an intelligent chatbot for realty that streamlines complaint management. The solution utilizes advanced Natural Language Processing (NLP) techniques for complaint reception, severity evaluation, and automated ticket generation. The system extracts key details from complaints, employs a structured questioning system for root cause identification, and integrates seamlessly with the ticketing system for swift issue resolution. Incorporating a troubleshooting document corpus further enhances problem-solving capabilities. Continuous learning mechanisms and modular architecture alongside human handover capability ensure scalability, user satisfaction, and streamlined processes. The project yielded improved operational efficiency, cost savings, data-driven insights, and enhanced responsiveness resulting in improved resident satisfaction.

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Business Requirement

The client required an intelligent chatbot that is capable of receiving, understanding, and processing complaints from residents. The proposed chatbot would autonomously generate its logic and decision trees by analyzing root cause flow diagrams associated with common complaints.

  • Complaint reception via text input
  • Severity evaluation to prioritize urgent issues
  • Automated ticket generation with detailed information for resolution
  • Utilization of document corpus for issue resolution guidance

QBurst Solution

A comprehensive chatbot solution leveraging NLP that incorporates components to handle resident inputs, assess complaints, and automate issue resolution.

NLP-based information extraction

Utilized NLP models to extract relevant information from resident inputs. Techniques such as Named Entity Recognition (NER) and sentiment analysis were employed to identify key entities, sentiments, and context from the complaints.

Structured questioning system

Implemented a structured questioning system to systematically identify the root cause and assess the severity of each complaint. A rule-based system is designed to ask targeted questions based on the type of complaint to gather specific details.

Auto generation of bot logic

Developed a unique capability to auto-generate bot logic using root cause flow diagrams. Leveraging the client's organizational knowledge, the system employs these diagrams to automatically configure the bot's decision-making processes, ensuring a tailored and precise approach to issue resolution.

Utilization of troubleshooting document corpus

Incorporated a comprehensive document corpus detailing troubleshooting procedures. We trained the chatbot using this corpus to aid in problem identification and resolution. Natural Language Understanding (NLU) models were fine-tuned using this corpus to improve the chatbot's ability to comprehend and address various issues.

Integration with ticketing system

Integrated the chatbot with a ticketing system to automate generation of tickets for issue resolution. Upon gathering necessary details, the chatbot creates tickets in the system and assigns them to relevant support personnel or departments based on the nature and severity of the issue.

Continuous learning and improvement

Implemented mechanisms for continuous learning and improvement. This involves collecting feedback from ticket resolutions to enhance the chatbot's accuracy and effectiveness over time. We employed techniques such as reinforcement learning to keep the system updated with new information and evolving issues.

Project Highlights

  • Modular Architecture
  • PII Data Filtering
  • Human Handover Capability
  • Admin user interface features
  • Performance monitoring and analytics
  • Scalability and Adaptability

Benefits

  • Efficient complaint handling
  • Improved satisfaction
  • Cost savings
  • Data-driven insights
  • Scalability and consistency
  • Standardized processes
  • Enhanced responsiveness

Technologies

  • OpenAI
  • Python
  • Java
  • Google Cloud Platform
  • FreeDolly
  • Flan T5
  • Stable LM
  • GuardRail
  • BetterPrompt
  • Guidance
  • LoRA
  • Lamini
  • LangChain
  • AutoGPT, LoopGPT
  • Camel

Business Requirement

The client required an intelligent chatbot that is capable of receiving, understanding, and processing complaints from residents. The proposed chatbot would autonomously generate its logic and decision trees by analyzing root cause flow diagrams associated with common complaints.

  • Complaint reception via text input
  • Severity evaluation to prioritize urgent issues
  • Automated ticket generation with detailed information for resolution
  • Utilization of document corpus for issue resolution guidance

QBurst Solution

A comprehensive chatbot solution leveraging NLP that incorporates components to handle resident inputs, assess complaints, and automate issue resolution.

NLP-based information extraction

Utilized NLP models to extract relevant information from resident inputs. Techniques such as Named Entity Recognition (NER) and sentiment analysis were employed to identify key entities, sentiments, and context from the complaints.

Structured questioning system

Implemented a structured questioning system to systematically identify the root cause and assess the severity of each complaint. A rule-based system is designed to ask targeted questions based on the type of complaint to gather specific details.

Auto generation of bot logic

Developed a unique capability to auto-generate bot logic using root cause flow diagrams. Leveraging the client's organizational knowledge, the system employs these diagrams to automatically configure the bot's decision-making processes, ensuring a tailored and precise approach to issue resolution.

Utilization of troubleshooting document corpus

Incorporated a comprehensive document corpus detailing troubleshooting procedures. We trained the chatbot using this corpus to aid in problem identification and resolution. Natural Language Understanding (NLU) models were fine-tuned using this corpus to improve the chatbot's ability to comprehend and address various issues.

Integration with ticketing system

Integrated the chatbot with a ticketing system to automate generation of tickets for issue resolution. Upon gathering necessary details, the chatbot creates tickets in the system and assigns them to relevant support personnel or departments based on the nature and severity of the issue.

Continuous learning and improvement

Implemented mechanisms for continuous learning and improvement. This involves collecting feedback from ticket resolutions to enhance the chatbot's accuracy and effectiveness over time. We employed techniques such as reinforcement learning to keep the system updated with new information and evolving issues.

Project Highlights

  • Modular Architecture
  • PII Data Filtering
  • Human Handover Capability
  • Admin user interface features
  • Performance monitoring and analytics
  • Scalability and Adaptability

Benefits

  • Efficient complaint handling
  • Improved satisfaction
  • Cost savings
  • Data-driven insights
  • Scalability and consistency
  • Standardized processes
  • Enhanced responsiveness

Technologies

  • OpenAI
  • Python
  • Java
  • Google Cloud Platform
  • FreeDolly
  • Flan T5
  • Stable LM
  • GuardRail
  • BetterPrompt
  • Guidance
  • LoRA
  • Lamini
  • LangChain
  • AutoGPT, LoopGPT
  • Camel

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